Senior Machine Learning Expert

Senior Machine Learning Expert (AI Training)What if your deep expertise in machine learning could directly shape how the world’s most advanced AI systems think, plan, and solve problems? We’re looking for Senior Machine Learning Experts to author the reasoning traces that teach LLMs how to reason step by step - turning your technical intuition into the training data that powers next-generation AI.About The RoleThis is a fully remote, flexible contract role designed for experienced ML practitioners who understand how models make decisions and want to influence that process from the inside.Organization: AlignerrType: Hourly ContractLocation: RemoteCommitment: *** hours/weekWhat You’ll DoAuthor complex, high-fidelity reasoning traces that document how an LLM should plan, use tools, and make decisions across sophisticated technical tasksBreak down intricate real-world problems into clear, structured, logical steps that models can learn fromDesign and contribute to data strategies that help LLMs navigate multi-step decision-making scenarios reliablyReview and refine reasoning traces for quality, completeness, and alignment with best practices in model trainingApply your architectural and systems-level ML knowledge to ensure traces reflect how expert practitioners actually thinkWho You AreExperienced in machine learning, AI research, or a closely related technical field - you understand how models reason and where they failSkilled at decomposing hard problems into clear, documented, logical stepsFamiliar with LLM evaluation methodologies, training pipelines, or RLHF-style workflowsDetail-oriented and rigorous - you care about getting things exactly rightComfortable working independently and asynchronously in a remote environmentNice to HavePrior experience in data annotation, data quality, or AI evaluation systemsTop-tier Kaggle competition results (Grandmaster or Master level), demonstrating elite-level understanding of model performance and feature engineeringBackground in NLP, reasoning systems, or agent-based model developmentExperience writing technical documentation or structured problem-solving guidesWhy Join UsWork on some of the most technically challenging AI problems in the industry alongside leading research labsFully remote and flexible - work when and where it suits youFreelance autonomy with the structure and purpose of meaningful, high-impact workGain deep, hands-on exposure to how frontier LLMs are trained and evaluatedPotential for ongoing work and contract extension as new projects launch#J-***-Ljbffr

This is a fully remote, flexible contract role designed for experienced ML practitioners who understand how models make decisions and want to influence that process from the inside., Experienced in machine learning, AI research, or a closely related technical field - you understand how models reason and where they fail Skilled at decomposing hard problems into clear, documented, logical steps Familiar with LLM evaluation methodologies, training pipelines, or RLHF-style workflows Detail-oriented and rigorous - you care about getting things exactly right Comfortable working independently and asynchronously in a remote environment Nice to Have Prior experience in data annotation, data quality, or AI evaluation systems Top-tier Kaggle competition results (Grandmaster or Master level), demonstrating elite-level understanding of model performance and feature engineering Background in NLP, reasoning systems, or agent-based model development Experience writing technical documentation or structured problem-solving guides

Work on some of the most technically challenging AI problems in the industry alongside leading research labs Fully remote and flexible - work when and where it suits you Freelance autonomy with the structure and purpose of meaningful, high-impact work Gain deep, hands-on exposure to how frontier LLMs are trained and evaluated Potential for ongoing work and contract extension as new projects launch #J-*****-Ljbffr

Author complex, high-fidelity reasoning traces that document how an LLM should plan, use tools, and make decisions across sophisticated technical tasks Break down intricate real-world problems into clear, structured, logical steps that models can learn from Design and contribute to data strategies that help LLMs navigate multi-step decision-making scenarios reliably Review and refine reasoning traces for quality, completeness, and alignment with best practices in model training Apply your architectural and systems-level ML knowledge to ensure traces reflect how expert practitioners actually think